Dense Segmentation

Dense segmentation aims to assign a semantic label to every pixel in an image, enabling fine-grained scene understanding. Current research focuses on improving accuracy and efficiency through various approaches, including leveraging pre-trained models like Segment Anything Model (SAM), combining deep learning with geometric models, and exploring weakly-supervised or unsupervised learning techniques to reduce annotation burden. These advancements are driving progress in applications such as autonomous driving, robotics, and remote sensing, where accurate and efficient scene understanding is crucial.